Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

BackgroundMultiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time x group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time x group variation if such variation was present in the data-generating model. MethodsContinuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time x group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time x group random effects. The analytic models were then reapplied without specifying random effects for time x group. ResultsResults indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time x group variance. ConclusionThe time x group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

Automated summary

This study compared the effectiveness of using RC models and RM-ANOVA models to analyze multiple-period parallel GRTs. The results showed that both RC and saturated analytic models maintained the nominal type I error rate in all data sets, while RM-ANOVA models with unstructured covariance exhibited inflation in type I error rates when applied to cohort data. In addition, analytic models that omitted time-varying group random effects were prone to substantial type I error inflation unless the residual error variance was high relative to the time x group variance.